These are the kind of experiences you can live in our environment if you stick long enough. All true, by the way, and I\u2019ve got quite a few more stories like that, but you wouldn\u2019t believe me.<\/p>\n
Most people are in the industry for the fascination, the travelling eagerness, the international ethos. How can we expect them to behave like scientists when checking company\u2019s performance and \u2013 more to the point- to make critical business decisions? In the travel industry, it is increasingly challenging to find individuals with a blend of business acumen, methodological expertise, and mathematical\/statistical aptitudes. If you’re intrigued by the article’s title, chances are you fall into this category \u2013 congratulations! It’s likely you’ve been pondering how to establish and sustain a data-driven culture in your organization.
\nBefore offering some tips, let’s address the three most significant roadblocks you’ll encounter.<\/p>\n
It takes time<\/strong><\/h4>\nHalf a year, at the very least. For all kinds of companies, adopting a data-driven culture takes time.<\/p>\n
\n- With accessible analytics tools these days, it’s vital to move beyond spreadsheets… and unused reports and dashboards.<\/li>\n
- Insights must drive action! they need to trigger action for real progress and growth, instead of relying on hunches.<\/li>\n
- Expect a learning phase, but soon, users will see tangible benefits. Everyone will start having a data-dependent mindset, and positive financial impact will be evident.<\/li>\n<\/ul>\n
You\u2019ll make enemies<\/strong><\/h4>\nYou’re bound to encounter resistance; people are generally averse to change. The challenge is breaking free from the comfort zone and outdated strategies. The transition to a data-driven culture faces its highest barrier here. Fear of the unknown intensifies the resistance, with concerns about job security and a shift towards a more technical environment. When you disrupt established procedures, expect opposition<\/strong> and, at times, hostility. Some may even perceive you as the enemy, especially those whose subpar performance is exposed by data analysis. Face the potential hate wave as an inevitable part of the process. In the worst-case scenario, if key decision-makers exhibit poor performance, diplomatically addressing the issues is a nightmare .What if data clearly shows their hunch-based decisions are damaging the company? Well, my friend, you have a very sensible diplomacy task ahead\u2026<\/p>\n![\"\"](\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/datadrv-4.jpg\")
\nData collection and processing = not easy<\/strong><\/h4>\nThe bigger your company, the harder it will be to organize, collect, store and process amounts of data that may become humongous. Even for a tiny DMC or family-owned hotel with only a booking engine and an PMS or ERP. I could write a whole series of articles on this subject, based only on my superficial knowledge of it! At the most basic level, data processing requires expert intervention to set-up a scalable system (because you\u2019re doing this to grow, right?) As well a monetary investment proportional to the project\u2019s scope. People love answers from data but hate the hassle of gathering, cleaning, and preparing it. A well-set-up system tackles this nuisance once, allowing you to almost forget about it. At least for a while, until your growing pains force an update.<\/p>\n
![\"\"](\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/datadrv-3.png\")
\nHere\u2019s the good news<\/h4>\n
Timelines, rooted bad habits and technical matters aside, the benefits from adopting a data-driven culture company-wide are absolutely worth the wait and the struggle. I suspect you know them already very well. It\u2019s scientifically proven (from financial as well as fiscal publicly available information) that companies operating under a data-driven culture boast lower overheads and diminished risks from decision making, thus consistently increasing profits. Google it if you don\u2019t believe my statement!<\/p>\n
\nThe real good news here is that, although it takes some sort of scientific approach to embrace a data-driven culture, it can be attained with reasonable effort, even in the glamour-ridden travel industry.<\/strong><\/p>\n<\/blockquote>\nPractical advice<\/h4>\n
Each company, each vertical would employ different methods and tools, there is no one-fits-all model. However, there are a few guidelines to create and embrace a data-driven culture within a travel (or any other) organization.<\/p>\n
One step at a time!<\/strong>
\nStart with one area or department critical to the company\u2019s performance. Or with a simple KPI exchange between departments, to find common ground and avoiding fault-searching and finger pointing among stakeholders.<\/p>\nPose a business challenge!<\/strong>
\nEstablish that analytics implementation should bring a 5% revenue improvement by year\u2019s end. It’s a reasonable objective (and realistic, I should add). However, instead of just pursuing objectives, try checking \u201cwhat-if<\/em>\u201d scenarios or run simulations based on the predictions bought by data.<\/p>\nMeasure!<\/strong>
\nThe point in collecting data is to measure parameters, to experiment and predict, to answer business questions. Not everything should or can be measured, though. Rather opt for \u201coutcome-based\u201d performance measures to begin with, the so called KPIs\u2026 Looking to maximize analytics ROI, focusing on KPI metrics is paramount, although it is a good idea to collect ALL possible data. You never know what might be valuable to measure in the future! And I\u2019m not referring only to quantifiable stuff, also qualitative measures should be accumulated. For instance, if you want to record certain predictions and have no numbers, use \u201clow\/medium\/high probability\u201d: better to have an approximation than not collecting data at all.<\/p>\nExperiment and Govern!<\/strong>
\nTo take data-driven decisions, you have to blindly trust the quality of your measures. If the prep was done correctly you shouldn\u2019t have to worry about it, right? Not so: always check for outliers, question everything and test alternatives. In short: act as a scientist. Before adopting a data driven-culture, everyone must absorbe at least some data literacy >>.<\/a> Establish what is a success beforehand, try something, measure its results, home in on the lessons learnt, rinse and repeat. This is especially true to verify predictions\u2019 accuracy!<\/p>\nNo experiment is a failure. Instead, it’s an opportunity for valuable lessons, amusement, and discovering new possibilities. Engaging staff in data-driven tasks not only makes their jobs easier, it also brings a sense of fun and competition, creating a motivated and efficient workplace.<\/p>\n
Fancify!<\/strong>
\nData and related activities are naturally boring for our flamboyant travel execs: ditch tables and spreadsheets, give them elegant flashy dashboards to play around, to easily and immediately visualize whatever data comparison, aggregation or cross-analysis they might need on their daily or monthly basis. Insights can be conveyed into images, or even whole stories, which can be presented with (or through) emotions. At the end of the day, people base their decisions on their feelings: if after having a go at the evidence brought by data they still choose to follow their instinct, it\u2019s their prerogative. Beware of the HiPPO effect >><\/a><\/strong>, though!<\/p>\n![\"\"](\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/datadrv-5.gif\")
<\/p>\n
Share!<\/strong> Every organization tends to compartmentalize information in silos (in the travel industry, even more so), and that\u2019s something to get rid of. A good idea for starters would be to run bi-weekly or monthly meetings in which all areas or departments exchange a general vision of their KPIs, their experiments results. The long-term objective will be to have a common repository of insights company-wide, so everyone can verify or cross-analyze their datasets against the big picture (company\u2019s general performance). Perhaps the financials officer can discover a way to improve operational aspects\u2026 or vice versa!<\/p>\n![\"\"](\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/datadrv-1.gif\")
<\/p>\n
Commit!<\/strong> There\u2019s no turning back, burn your Excel ships. Make it abundantly clear to everybody that from now on, every decision, every course of action, must be backed up by data. Psychology of resistance is really hard to eradicate, more so in large companies, but if you are the decision-maker you\u2019d better start educating by example and acting based on what data brought forward. If you\u2019re just the main analyst, your superiors must accept that the truth is not whatever their judgement says: truth lies in experiments results (or very close to them).<\/p>\n![\"\"](\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/data-new-oil.jpg\")
<\/p>\n
A few final pointers<\/strong><\/h4>\nIf you’re overwhelmed by a mountain of data and uncertain where to begin, consider BIFLIX.<\/strong> The subscription offering includes consultancy for small projects. We assist in establishing your data infrastructure, developing a long-term data strategy, and providing essential training for you and your staff to interpret and act on analytics results.<\/span><\/p>\nFinding dedicated data experts for long-term employment is challenging, especially given the nature of millennials and the probable lack of constant demand for a full-time team, unless you’re like TUI or Hotelbeds. For small to medium-sized businesses, fostering a data-driven culture among existing and new staff is key. When system updates are required, specialized third-party assistance can be enlisted, paying only for the specific project. Unless you opt for BIFLIX<\/strong>, eliminating scalability worries.<\/p>\nIf convincing staff or leaders of the advantages of adopting a data-driven culture is challenging, showcase the financial benefits it brings to the company, investors, and clients. A fundamental task of analytics is \u201cdata mining\u201d. Know why? They\u2019re digging for GOLD, not for the whitened bones of the \u201cthis is the way we\u2019ve always done it<\/em>\u201d extinct defenders. Allow me a clich\u00e9 here: data is the new oil.<\/p>\nIn fact, with no data-driven culture in place, it would be impossible to implement revenue management strategies >><\/a>, dynamic pricing >><\/a>, etc. Like it or not, it’s a question of survival in today’s harsh competitive environment.<\/p>\n![\"\"](\"http:\/\/revva.net\/wp-content\/uploads\/2018\/04\/datadrv-opinion.jpg\")
\nFinally, always keep in mind that data drives no company: people does<\/strong>. Machines and artificial intelligence can feed on data to help see the big picture, check the company\u2019s health and predict outcomes, but ultimately the decision lies on your lap (or your boss\u2019).<\/p>\nHow do I know all this?<\/strong><\/h4>\nBecause I tried and FAILED<\/strong>, again and again. I studied dozens of data-driven companies (travel related or not) and attempted to implement some sort of data-driven culture in several types and sizes of operators and hotels for years, failing miserably until I started to learn from my mistakes. Maybe I was ahead of my time, most probably I am a slow learner. But to my meagre satisfaction, thanks surely to buzzword spreading, I\u2019m noticing more and more interest in the travel arena towards data monetization. About time!
\nDid you try all this in your organization? Are you planning to do it soon? Drop me a line, I\u2019d love to share experiences<\/p>\nThanks for reading!<\/p>\n
Marcello Bresin<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"Not easy, but not as difficult as it may seem. Give it a try, you need data culture!<\/p>\n","protected":false},"author":1,"featured_media":15055,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[70],"tags":[74,71],"class_list":["post-12038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-analytics","tag-bi"],"acf":[],"yoast_head":"\n
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You\u2019ll make enemies<\/strong><\/h4>\nYou’re bound to encounter resistance; people are generally averse to change. The challenge is breaking free from the comfort zone and outdated strategies. The transition to a data-driven culture faces its highest barrier here. Fear of the unknown intensifies the resistance, with concerns about job security and a shift towards a more technical environment. When you disrupt established procedures, expect opposition<\/strong> and, at times, hostility. Some may even perceive you as the enemy, especially those whose subpar performance is exposed by data analysis. Face the potential hate wave as an inevitable part of the process. In the worst-case scenario, if key decision-makers exhibit poor performance, diplomatically addressing the issues is a nightmare .What if data clearly shows their hunch-based decisions are damaging the company? Well, my friend, you have a very sensible diplomacy task ahead\u2026<\/p>\n
\nData collection and processing = not easy<\/strong><\/h4>\nThe bigger your company, the harder it will be to organize, collect, store and process amounts of data that may become humongous. Even for a tiny DMC or family-owned hotel with only a booking engine and an PMS or ERP. I could write a whole series of articles on this subject, based only on my superficial knowledge of it! At the most basic level, data processing requires expert intervention to set-up a scalable system (because you\u2019re doing this to grow, right?) As well a monetary investment proportional to the project\u2019s scope. People love answers from data but hate the hassle of gathering, cleaning, and preparing it. A well-set-up system tackles this nuisance once, allowing you to almost forget about it. At least for a while, until your growing pains force an update.<\/p>\n
\nHere\u2019s the good news<\/h4>\n
Timelines, rooted bad habits and technical matters aside, the benefits from adopting a data-driven culture company-wide are absolutely worth the wait and the struggle. I suspect you know them already very well. It\u2019s scientifically proven (from financial as well as fiscal publicly available information) that companies operating under a data-driven culture boast lower overheads and diminished risks from decision making, thus consistently increasing profits. Google it if you don\u2019t believe my statement!<\/p>\n
\nThe real good news here is that, although it takes some sort of scientific approach to embrace a data-driven culture, it can be attained with reasonable effort, even in the glamour-ridden travel industry.<\/strong><\/p>\n<\/blockquote>\nPractical advice<\/h4>\n
Each company, each vertical would employ different methods and tools, there is no one-fits-all model. However, there are a few guidelines to create and embrace a data-driven culture within a travel (or any other) organization.<\/p>\n
One step at a time!<\/strong>
\nStart with one area or department critical to the company\u2019s performance. Or with a simple KPI exchange between departments, to find common ground and avoiding fault-searching and finger pointing among stakeholders.<\/p>\nPose a business challenge!<\/strong>
\nEstablish that analytics implementation should bring a 5% revenue improvement by year\u2019s end. It’s a reasonable objective (and realistic, I should add). However, instead of just pursuing objectives, try checking \u201cwhat-if<\/em>\u201d scenarios or run simulations based on the predictions bought by data.<\/p>\nMeasure!<\/strong>
\nThe point in collecting data is to measure parameters, to experiment and predict, to answer business questions. Not everything should or can be measured, though. Rather opt for \u201coutcome-based\u201d performance measures to begin with, the so called KPIs\u2026 Looking to maximize analytics ROI, focusing on KPI metrics is paramount, although it is a good idea to collect ALL possible data. You never know what might be valuable to measure in the future! And I\u2019m not referring only to quantifiable stuff, also qualitative measures should be accumulated. For instance, if you want to record certain predictions and have no numbers, use \u201clow\/medium\/high probability\u201d: better to have an approximation than not collecting data at all.<\/p>\nExperiment and Govern!<\/strong>
\nTo take data-driven decisions, you have to blindly trust the quality of your measures. If the prep was done correctly you shouldn\u2019t have to worry about it, right? Not so: always check for outliers, question everything and test alternatives. In short: act as a scientist. Before adopting a data driven-culture, everyone must absorbe at least some data literacy >>.<\/a> Establish what is a success beforehand, try something, measure its results, home in on the lessons learnt, rinse and repeat. This is especially true to verify predictions\u2019 accuracy!<\/p>\nNo experiment is a failure. Instead, it’s an opportunity for valuable lessons, amusement, and discovering new possibilities. Engaging staff in data-driven tasks not only makes their jobs easier, it also brings a sense of fun and competition, creating a motivated and efficient workplace.<\/p>\n
Fancify!<\/strong>
\nData and related activities are naturally boring for our flamboyant travel execs: ditch tables and spreadsheets, give them elegant flashy dashboards to play around, to easily and immediately visualize whatever data comparison, aggregation or cross-analysis they might need on their daily or monthly basis. Insights can be conveyed into images, or even whole stories, which can be presented with (or through) emotions. At the end of the day, people base their decisions on their feelings: if after having a go at the evidence brought by data they still choose to follow their instinct, it\u2019s their prerogative. Beware of the HiPPO effect >><\/a><\/strong>, though!<\/p>\n<\/p>\n
Share!<\/strong> Every organization tends to compartmentalize information in silos (in the travel industry, even more so), and that\u2019s something to get rid of. A good idea for starters would be to run bi-weekly or monthly meetings in which all areas or departments exchange a general vision of their KPIs, their experiments results. The long-term objective will be to have a common repository of insights company-wide, so everyone can verify or cross-analyze their datasets against the big picture (company\u2019s general performance). Perhaps the financials officer can discover a way to improve operational aspects\u2026 or vice versa!<\/p>\n<\/p>\n
Commit!<\/strong> There\u2019s no turning back, burn your Excel ships. Make it abundantly clear to everybody that from now on, every decision, every course of action, must be backed up by data. Psychology of resistance is really hard to eradicate, more so in large companies, but if you are the decision-maker you\u2019d better start educating by example and acting based on what data brought forward. If you\u2019re just the main analyst, your superiors must accept that the truth is not whatever their judgement says: truth lies in experiments results (or very close to them).<\/p>\n<\/p>\n
A few final pointers<\/strong><\/h4>\nIf you’re overwhelmed by a mountain of data and uncertain where to begin, consider BIFLIX.<\/strong> The subscription offering includes consultancy for small projects. We assist in establishing your data infrastructure, developing a long-term data strategy, and providing essential training for you and your staff to interpret and act on analytics results.<\/span><\/p>\nFinding dedicated data experts for long-term employment is challenging, especially given the nature of millennials and the probable lack of constant demand for a full-time team, unless you’re like TUI or Hotelbeds. For small to medium-sized businesses, fostering a data-driven culture among existing and new staff is key. When system updates are required, specialized third-party assistance can be enlisted, paying only for the specific project. Unless you opt for BIFLIX<\/strong>, eliminating scalability worries.<\/p>\nIf convincing staff or leaders of the advantages of adopting a data-driven culture is challenging, showcase the financial benefits it brings to the company, investors, and clients. A fundamental task of analytics is \u201cdata mining\u201d. Know why? They\u2019re digging for GOLD, not for the whitened bones of the \u201cthis is the way we\u2019ve always done it<\/em>\u201d extinct defenders. Allow me a clich\u00e9 here: data is the new oil.<\/p>\nIn fact, with no data-driven culture in place, it would be impossible to implement revenue management strategies >><\/a>, dynamic pricing >><\/a>, etc. Like it or not, it’s a question of survival in today’s harsh competitive environment.<\/p>\n
\nFinally, always keep in mind that data drives no company: people does<\/strong>. Machines and artificial intelligence can feed on data to help see the big picture, check the company\u2019s health and predict outcomes, but ultimately the decision lies on your lap (or your boss\u2019).<\/p>\nHow do I know all this?<\/strong><\/h4>\nBecause I tried and FAILED<\/strong>, again and again. I studied dozens of data-driven companies (travel related or not) and attempted to implement some sort of data-driven culture in several types and sizes of operators and hotels for years, failing miserably until I started to learn from my mistakes. Maybe I was ahead of my time, most probably I am a slow learner. But to my meagre satisfaction, thanks surely to buzzword spreading, I\u2019m noticing more and more interest in the travel arena towards data monetization. About time!
\nDid you try all this in your organization? Are you planning to do it soon? Drop me a line, I\u2019d love to share experiences<\/p>\nThanks for reading!<\/p>\n
Marcello Bresin<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"Not easy, but not as difficult as it may seem. Give it a try, you need data culture!<\/p>\n","protected":false},"author":1,"featured_media":15055,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[70],"tags":[74,71],"class_list":["post-12038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-analytics","tag-bi"],"acf":[],"yoast_head":"\n
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\nData collection and processing = not easy<\/strong><\/h4>\nThe bigger your company, the harder it will be to organize, collect, store and process amounts of data that may become humongous. Even for a tiny DMC or family-owned hotel with only a booking engine and an PMS or ERP. I could write a whole series of articles on this subject, based only on my superficial knowledge of it! At the most basic level, data processing requires expert intervention to set-up a scalable system (because you\u2019re doing this to grow, right?) As well a monetary investment proportional to the project\u2019s scope. People love answers from data but hate the hassle of gathering, cleaning, and preparing it. A well-set-up system tackles this nuisance once, allowing you to almost forget about it. At least for a while, until your growing pains force an update.<\/p>\n
\nHere\u2019s the good news<\/h4>\n
Timelines, rooted bad habits and technical matters aside, the benefits from adopting a data-driven culture company-wide are absolutely worth the wait and the struggle. I suspect you know them already very well. It\u2019s scientifically proven (from financial as well as fiscal publicly available information) that companies operating under a data-driven culture boast lower overheads and diminished risks from decision making, thus consistently increasing profits. Google it if you don\u2019t believe my statement!<\/p>\n
\nThe real good news here is that, although it takes some sort of scientific approach to embrace a data-driven culture, it can be attained with reasonable effort, even in the glamour-ridden travel industry.<\/strong><\/p>\n<\/blockquote>\nPractical advice<\/h4>\n
Each company, each vertical would employ different methods and tools, there is no one-fits-all model. However, there are a few guidelines to create and embrace a data-driven culture within a travel (or any other) organization.<\/p>\n
One step at a time!<\/strong>
\nStart with one area or department critical to the company\u2019s performance. Or with a simple KPI exchange between departments, to find common ground and avoiding fault-searching and finger pointing among stakeholders.<\/p>\nPose a business challenge!<\/strong>
\nEstablish that analytics implementation should bring a 5% revenue improvement by year\u2019s end. It’s a reasonable objective (and realistic, I should add). However, instead of just pursuing objectives, try checking \u201cwhat-if<\/em>\u201d scenarios or run simulations based on the predictions bought by data.<\/p>\nMeasure!<\/strong>
\nThe point in collecting data is to measure parameters, to experiment and predict, to answer business questions. Not everything should or can be measured, though. Rather opt for \u201coutcome-based\u201d performance measures to begin with, the so called KPIs\u2026 Looking to maximize analytics ROI, focusing on KPI metrics is paramount, although it is a good idea to collect ALL possible data. You never know what might be valuable to measure in the future! And I\u2019m not referring only to quantifiable stuff, also qualitative measures should be accumulated. For instance, if you want to record certain predictions and have no numbers, use \u201clow\/medium\/high probability\u201d: better to have an approximation than not collecting data at all.<\/p>\nExperiment and Govern!<\/strong>
\nTo take data-driven decisions, you have to blindly trust the quality of your measures. If the prep was done correctly you shouldn\u2019t have to worry about it, right? Not so: always check for outliers, question everything and test alternatives. In short: act as a scientist. Before adopting a data driven-culture, everyone must absorbe at least some data literacy >>.<\/a> Establish what is a success beforehand, try something, measure its results, home in on the lessons learnt, rinse and repeat. This is especially true to verify predictions\u2019 accuracy!<\/p>\nNo experiment is a failure. Instead, it’s an opportunity for valuable lessons, amusement, and discovering new possibilities. Engaging staff in data-driven tasks not only makes their jobs easier, it also brings a sense of fun and competition, creating a motivated and efficient workplace.<\/p>\n
Fancify!<\/strong>
\nData and related activities are naturally boring for our flamboyant travel execs: ditch tables and spreadsheets, give them elegant flashy dashboards to play around, to easily and immediately visualize whatever data comparison, aggregation or cross-analysis they might need on their daily or monthly basis. Insights can be conveyed into images, or even whole stories, which can be presented with (or through) emotions. At the end of the day, people base their decisions on their feelings: if after having a go at the evidence brought by data they still choose to follow their instinct, it\u2019s their prerogative. Beware of the HiPPO effect >><\/a><\/strong>, though!<\/p>\n<\/p>\n
Share!<\/strong> Every organization tends to compartmentalize information in silos (in the travel industry, even more so), and that\u2019s something to get rid of. A good idea for starters would be to run bi-weekly or monthly meetings in which all areas or departments exchange a general vision of their KPIs, their experiments results. The long-term objective will be to have a common repository of insights company-wide, so everyone can verify or cross-analyze their datasets against the big picture (company\u2019s general performance). Perhaps the financials officer can discover a way to improve operational aspects\u2026 or vice versa!<\/p>\n<\/p>\n
Commit!<\/strong> There\u2019s no turning back, burn your Excel ships. Make it abundantly clear to everybody that from now on, every decision, every course of action, must be backed up by data. Psychology of resistance is really hard to eradicate, more so in large companies, but if you are the decision-maker you\u2019d better start educating by example and acting based on what data brought forward. If you\u2019re just the main analyst, your superiors must accept that the truth is not whatever their judgement says: truth lies in experiments results (or very close to them).<\/p>\n<\/p>\n
A few final pointers<\/strong><\/h4>\nIf you’re overwhelmed by a mountain of data and uncertain where to begin, consider BIFLIX.<\/strong> The subscription offering includes consultancy for small projects. We assist in establishing your data infrastructure, developing a long-term data strategy, and providing essential training for you and your staff to interpret and act on analytics results.<\/span><\/p>\nFinding dedicated data experts for long-term employment is challenging, especially given the nature of millennials and the probable lack of constant demand for a full-time team, unless you’re like TUI or Hotelbeds. For small to medium-sized businesses, fostering a data-driven culture among existing and new staff is key. When system updates are required, specialized third-party assistance can be enlisted, paying only for the specific project. Unless you opt for BIFLIX<\/strong>, eliminating scalability worries.<\/p>\nIf convincing staff or leaders of the advantages of adopting a data-driven culture is challenging, showcase the financial benefits it brings to the company, investors, and clients. A fundamental task of analytics is \u201cdata mining\u201d. Know why? They\u2019re digging for GOLD, not for the whitened bones of the \u201cthis is the way we\u2019ve always done it<\/em>\u201d extinct defenders. Allow me a clich\u00e9 here: data is the new oil.<\/p>\nIn fact, with no data-driven culture in place, it would be impossible to implement revenue management strategies >><\/a>, dynamic pricing >><\/a>, etc. Like it or not, it’s a question of survival in today’s harsh competitive environment.<\/p>\n
\nFinally, always keep in mind that data drives no company: people does<\/strong>. Machines and artificial intelligence can feed on data to help see the big picture, check the company\u2019s health and predict outcomes, but ultimately the decision lies on your lap (or your boss\u2019).<\/p>\nHow do I know all this?<\/strong><\/h4>\nBecause I tried and FAILED<\/strong>, again and again. I studied dozens of data-driven companies (travel related or not) and attempted to implement some sort of data-driven culture in several types and sizes of operators and hotels for years, failing miserably until I started to learn from my mistakes. Maybe I was ahead of my time, most probably I am a slow learner. But to my meagre satisfaction, thanks surely to buzzword spreading, I\u2019m noticing more and more interest in the travel arena towards data monetization. About time!
\nDid you try all this in your organization? Are you planning to do it soon? Drop me a line, I\u2019d love to share experiences<\/p>\nThanks for reading!<\/p>\n
Marcello Bresin<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"Not easy, but not as difficult as it may seem. Give it a try, you need data culture!<\/p>\n","protected":false},"author":1,"featured_media":15055,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[70],"tags":[74,71],"class_list":["post-12038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-analytics","tag-bi"],"acf":[],"yoast_head":"\n
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\nHere\u2019s the good news<\/h4>\n
The real good news here is that, although it takes some sort of scientific approach to embrace a data-driven culture, it can be attained with reasonable effort, even in the glamour-ridden travel industry.<\/strong><\/p>\n<\/blockquote>\n Each company, each vertical would employ different methods and tools, there is no one-fits-all model. However, there are a few guidelines to create and embrace a data-driven culture within a travel (or any other) organization.<\/p>\n One step at a time!<\/strong> Pose a business challenge!<\/strong> Measure!<\/strong> Experiment and Govern!<\/strong> No experiment is a failure. Instead, it’s an opportunity for valuable lessons, amusement, and discovering new possibilities. Engaging staff in data-driven tasks not only makes their jobs easier, it also brings a sense of fun and competition, creating a motivated and efficient workplace.<\/p>\n Fancify!<\/strong> Share!<\/strong> Every organization tends to compartmentalize information in silos (in the travel industry, even more so), and that\u2019s something to get rid of. A good idea for starters would be to run bi-weekly or monthly meetings in which all areas or departments exchange a general vision of their KPIs, their experiments results. The long-term objective will be to have a common repository of insights company-wide, so everyone can verify or cross-analyze their datasets against the big picture (company\u2019s general performance). Perhaps the financials officer can discover a way to improve operational aspects\u2026 or vice versa!<\/p>\n Commit!<\/strong> There\u2019s no turning back, burn your Excel ships. Make it abundantly clear to everybody that from now on, every decision, every course of action, must be backed up by data. Psychology of resistance is really hard to eradicate, more so in large companies, but if you are the decision-maker you\u2019d better start educating by example and acting based on what data brought forward. If you\u2019re just the main analyst, your superiors must accept that the truth is not whatever their judgement says: truth lies in experiments results (or very close to them).<\/p>\n If you’re overwhelmed by a mountain of data and uncertain where to begin, consider BIFLIX.<\/strong> The subscription offering includes consultancy for small projects. We assist in establishing your data infrastructure, developing a long-term data strategy, and providing essential training for you and your staff to interpret and act on analytics results.<\/span><\/p>\n Finding dedicated data experts for long-term employment is challenging, especially given the nature of millennials and the probable lack of constant demand for a full-time team, unless you’re like TUI or Hotelbeds. For small to medium-sized businesses, fostering a data-driven culture among existing and new staff is key. When system updates are required, specialized third-party assistance can be enlisted, paying only for the specific project. Unless you opt for BIFLIX<\/strong>, eliminating scalability worries.<\/p>\n If convincing staff or leaders of the advantages of adopting a data-driven culture is challenging, showcase the financial benefits it brings to the company, investors, and clients. A fundamental task of analytics is \u201cdata mining\u201d. Know why? They\u2019re digging for GOLD, not for the whitened bones of the \u201cthis is the way we\u2019ve always done it<\/em>\u201d extinct defenders. Allow me a clich\u00e9 here: data is the new oil.<\/p>\n In fact, with no data-driven culture in place, it would be impossible to implement revenue management strategies >><\/a>, dynamic pricing >><\/a>, etc. Like it or not, it’s a question of survival in today’s harsh competitive environment.<\/p>\n Because I tried and FAILED<\/strong>, again and again. I studied dozens of data-driven companies (travel related or not) and attempted to implement some sort of data-driven culture in several types and sizes of operators and hotels for years, failing miserably until I started to learn from my mistakes. Maybe I was ahead of my time, most probably I am a slow learner. But to my meagre satisfaction, thanks surely to buzzword spreading, I\u2019m noticing more and more interest in the travel arena towards data monetization. About time! Thanks for reading!<\/p>\n Marcello Bresin<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Not easy, but not as difficult as it may seem. Give it a try, you need data culture!<\/p>\n","protected":false},"author":1,"featured_media":15055,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[70],"tags":[74,71],"class_list":["post-12038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-analytics","tag-bi"],"acf":[],"yoast_head":"\nPractical advice<\/h4>\n
\nStart with one area or department critical to the company\u2019s performance. Or with a simple KPI exchange between departments, to find common ground and avoiding fault-searching and finger pointing among stakeholders.<\/p>\n
\nEstablish that analytics implementation should bring a 5% revenue improvement by year\u2019s end. It’s a reasonable objective (and realistic, I should add). However, instead of just pursuing objectives, try checking \u201cwhat-if<\/em>\u201d scenarios or run simulations based on the predictions bought by data.<\/p>\n
\nThe point in collecting data is to measure parameters, to experiment and predict, to answer business questions. Not everything should or can be measured, though. Rather opt for \u201coutcome-based\u201d performance measures to begin with, the so called KPIs\u2026 Looking to maximize analytics ROI, focusing on KPI metrics is paramount, although it is a good idea to collect ALL possible data. You never know what might be valuable to measure in the future! And I\u2019m not referring only to quantifiable stuff, also qualitative measures should be accumulated. For instance, if you want to record certain predictions and have no numbers, use \u201clow\/medium\/high probability\u201d: better to have an approximation than not collecting data at all.<\/p>\n
\nTo take data-driven decisions, you have to blindly trust the quality of your measures. If the prep was done correctly you shouldn\u2019t have to worry about it, right? Not so: always check for outliers, question everything and test alternatives. In short: act as a scientist. Before adopting a data driven-culture, everyone must absorbe at least some data literacy >>.<\/a> Establish what is a success beforehand, try something, measure its results, home in on the lessons learnt, rinse and repeat. This is especially true to verify predictions\u2019 accuracy!<\/p>\n
\nData and related activities are naturally boring for our flamboyant travel execs: ditch tables and spreadsheets, give them elegant flashy dashboards to play around, to easily and immediately visualize whatever data comparison, aggregation or cross-analysis they might need on their daily or monthly basis. Insights can be conveyed into images, or even whole stories, which can be presented with (or through) emotions. At the end of the day, people base their decisions on their feelings: if after having a go at the evidence brought by data they still choose to follow their instinct, it\u2019s their prerogative. Beware of the HiPPO effect >><\/a><\/strong>, though!<\/p>\n<\/p>\n<\/p>\n<\/p>\nA few final pointers<\/strong><\/h4>\n
\nFinally, always keep in mind that data drives no company: people does<\/strong>. Machines and artificial intelligence can feed on data to help see the big picture, check the company\u2019s health and predict outcomes, but ultimately the decision lies on your lap (or your boss\u2019).<\/p>\nHow do I know all this?<\/strong><\/h4>\n
\nDid you try all this in your organization? Are you planning to do it soon? Drop me a line, I\u2019d love to share experiences<\/p>\n